Hi Everyone
I am Joost (pronounce ‘Yoast’) van Amersfoort (which is a Dutch city) and I am currently pursuing a PhD at the Unversity of Oxford under supervision of Professor Yarin Gal (in OATML) and Professor Yee Whye Teh (in OxCSML).
I am interested in variational inference (for example done through the reparametrisation trick, dropout and reversible models) and its applications such as uncertainty estimation, active learning and generative modelling.
Previously, I was a research engineer at Twitter Cortex, where I worked on large scale video models and recommendation systems. I did my masters at the University of Amsterdam, where I worked with Dr. Durk Kingma and Professor Max Welling. I've also done an internship at Facebook AI Research (FAIR).
Publications
Recent publications/preprints, for a full overview see my Google Scholar page.
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Uncertainty Estimation Using a Single Deep Deterministic Neural Network
Joost van Amersfoort, Lewis Smith, Yee Whye Teh, Yarin Gal
ICML 2020 - Video, Code, BibTex@article{van2020uncertainty, title={Uncertainty Estimation Using a Single Deep Deterministic Neural Network}, author={van Amersfoort, Joost and Smith, Lewis and Teh, Yee Whye and Gal, Yarin}, booktitle={International Conference on Machine Learning}, year={2020} } -
BatchBALD: Efficient and Diverse Batch Acquisition for Deep
Bayesian Active Learning
Andreas Kirsch*, Joost van Amersfoort*, Yarin Gal
NeurIPS 2019 - Blog, Poster, Code, Tutorial, BibTex@article{kirsch2019batchbald, title={BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning}, author={Kirsch, Andreas and van Amersfoort, Joost and Gal, Yarin}, booktitle={Advances in Neural Information Processing Systems}, year={2019} }
Reviewer
Blog Posts
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Variational
Inference in Sparse Gaussian Processes and Variational
Auto-Encoder
The post explains variational inference in sparse GPs from a VAE perspective.
Open Source - projects and reproductions
For a full overview of all my open source work, see my Github account.
- FastMNIST: The default PyTorch MNIST dataset implementation is slow when used on a modern GPU. This replacement speeds up training by 2-3x.
- Glow in PyTorch: The code reproduces results from "Do Deep Generative Models Know What They Don't Know?" and includes a pretrained model, evaluation notebooks and training code.
- CIFAR-10 baseline script: A self-contained, 150 line script that trains a ResNet-18 to ~94% accuracy on CIFAR-10 using PyTorch. Useful for obtaining a strong baseline with minimal tricks.